Dynamic Structural Equation Models with Missing Data: Data Requirements on <i>N</i> and <i>T</i>

Dynamic structural equation modeling (DSEM) is a useful technique for analyzing intensive longitudinal data. A challenge of applying DSEM is the missing data problem. The impact of missing data on DSEM, especially on widely applied DSEM such as the two-level vector autoregressive (VAR) cross-lagged models, however, is understudied. To fill the research gap, we evaluated how well the fixed effects and variance parameters in two-level bivariate VAR models are recovered under different missingness percentages, sample sizes, the number of time points, and heterogeneity in missingness distributions through two simulation studies. To facilitate the use of DSEM under customized data and model scenarios (different from those in our simulations), we provided illustrative examples of how to conduct Monte Carlo simulations in M<i>plus</i> to determine whether a data configuration is sufficient to obtain accurate and precise results from a specific DSEM.动态结构方程模型（Dynamic structural equation modeling, DSEM）是分析密集型纵向数据的常用技术。应用DSEM的一大挑战在于缺失数据问题，然而目前针对缺失数据对DSEM的影响，尤其是对两水平向量自回归（Vector Autoregressive, VAR）交叉滞后模型这类广泛使用的DSEM的影响的研究仍较为匮乏。为填补这一研究空白，本研究通过两项模拟研究，评估了在不同缺失率、样本量、时间点数目以及缺失分布异质性条件下，两水平双变量VAR模型中的固定效应与方差参数的恢复效果。为方便研究者在自定义数据与模型场景（区别于本研究的模拟场景）下使用DSEM，我们还提供了实操示例，演示如何在Mplus中开展蒙特卡洛模拟，以判断某一数据配置是否足以从特定DSEM中获得准确且精准的研究结果。